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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pub-attribs">Public 属性</a> &#124;
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<div class="title">pcl::ism::ImplicitShapeModelEstimation&lt; FeatureSize, PointT, NormalT &gt;::TC结构体 参考</div>  </div>
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<p>This structure is used for determining the end of the k-means clustering process.  
 <a href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="implicit__shape__model_8h_source.html">implicit_shape_model.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public 类型</h2></td></tr>
<tr class="memitem:ad320714a3552866232c707fe8a82f134"><td class="memItemLeft" align="right" valign="top"><a id="ad320714a3552866232c707fe8a82f134"></a>enum &#160;</td><td class="memItemRight" valign="bottom">{ <b>COUNT</b> = 1
, <b>EPS</b> = 2
 }</td></tr>
<tr class="separator:ad320714a3552866232c707fe8a82f134"><td class="memSeparator" colspan="2">&#160;</td></tr>
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Public 成员函数</h2></td></tr>
<tr class="memitem:a43b18d66f16d1075c762fa2a29007f14"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a43b18d66f16d1075c762fa2a29007f14">TC</a> (int type, int max_count, float epsilon)</td></tr>
<tr class="memdesc:a43b18d66f16d1075c762fa2a29007f14"><td class="mdescLeft">&#160;</td><td class="mdescRight">Termination criteria constructor.  <a href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a43b18d66f16d1075c762fa2a29007f14">更多...</a><br /></td></tr>
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Public 属性</h2></td></tr>
<tr class="memitem:a0d8d28702f28bd977f10f142c27ad25a"><td class="memItemLeft" align="right" valign="top"><a id="a0d8d28702f28bd977f10f142c27ad25a"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a0d8d28702f28bd977f10f142c27ad25a">type_</a></td></tr>
<tr class="memdesc:a0d8d28702f28bd977f10f142c27ad25a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Flag that determines when the k-means clustering must be stopped. If type_ equals COUNT then it must be stopped when the max number of iterations will be reached. If type_ eaquals EPS then it must be stopped when the desired accuracy will be reached. These flags can be used together, in that case the clustering will be finished when one of these conditions will be reached. <br /></td></tr>
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<tr class="memitem:abeeaa2e50bce56d045bdb78f43111250"><td class="memItemLeft" align="right" valign="top"><a id="abeeaa2e50bce56d045bdb78f43111250"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#abeeaa2e50bce56d045bdb78f43111250">max_count_</a></td></tr>
<tr class="memdesc:abeeaa2e50bce56d045bdb78f43111250"><td class="mdescLeft">&#160;</td><td class="mdescRight">Defines maximum number of iterations for k-means clustering. <br /></td></tr>
<tr class="separator:abeeaa2e50bce56d045bdb78f43111250"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6d1ca48170362af7b4f563067584769b"><td class="memItemLeft" align="right" valign="top"><a id="a6d1ca48170362af7b4f563067584769b"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a6d1ca48170362af7b4f563067584769b">epsilon_</a></td></tr>
<tr class="memdesc:a6d1ca48170362af7b4f563067584769b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Defines the accuracy for k-means clustering. <br /></td></tr>
<tr class="separator:a6d1ca48170362af7b4f563067584769b"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;int FeatureSize, typename PointT, typename NormalT = pcl::Normal&gt;<br />
struct pcl::ism::ImplicitShapeModelEstimation&lt; FeatureSize, PointT, NormalT &gt;::TC</h3>

<p>This structure is used for determining the end of the k-means clustering process. </p>
</div><h2 class="groupheader">构造及析构函数说明</h2>
<a id="a43b18d66f16d1075c762fa2a29007f14"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a43b18d66f16d1075c762fa2a29007f14">&#9670;&nbsp;</a></span>TC()</h2>

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template&lt;int FeatureSize, typename PointT , typename NormalT  = pcl::Normal&gt; </div>
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      <table class="memname">
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          <td class="memname"><a class="el" href="classpcl_1_1ism_1_1_implicit_shape_model_estimation.html">pcl::ism::ImplicitShapeModelEstimation</a>&lt; FeatureSize, <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a>, <a class="el" href="structpcl_1_1_normal.html">NormalT</a> &gt;::TC::TC </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>type</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>max_count</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>epsilon</em>&#160;</td>
        </tr>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Termination criteria constructor. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">type</td><td>defines the condition of termination(max iter., desired accuracy) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_count</td><td>defines the max number of iterations </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">epsilon</td><td>defines the desired accuracy </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;                                                     :</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;            <a class="code" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a0d8d28702f28bd977f10f142c27ad25a">type_</a> (type),</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;            <a class="code" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#abeeaa2e50bce56d045bdb78f43111250">max_count_</a> (max_count),</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;            <a class="code" href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a6d1ca48170362af7b4f563067584769b">epsilon_</a> (epsilon) {};</div>
<div class="ttc" id="astructpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c_html_a0d8d28702f28bd977f10f142c27ad25a"><div class="ttname"><a href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a0d8d28702f28bd977f10f142c27ad25a">pcl::ism::ImplicitShapeModelEstimation::TC::type_</a></div><div class="ttdeci">int type_</div><div class="ttdoc">Flag that determines when the k-means clustering must be stopped. If type_ equals COUNT then it must ...</div><div class="ttdef"><b>Definition:</b> implicit_shape_model.h:295</div></div>
<div class="ttc" id="astructpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c_html_a6d1ca48170362af7b4f563067584769b"><div class="ttname"><a href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#a6d1ca48170362af7b4f563067584769b">pcl::ism::ImplicitShapeModelEstimation::TC::epsilon_</a></div><div class="ttdeci">float epsilon_</div><div class="ttdoc">Defines the accuracy for k-means clustering.</div><div class="ttdef"><b>Definition:</b> implicit_shape_model.h:309</div></div>
<div class="ttc" id="astructpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c_html_abeeaa2e50bce56d045bdb78f43111250"><div class="ttname"><a href="structpcl_1_1ism_1_1_implicit_shape_model_estimation_1_1_t_c.html#abeeaa2e50bce56d045bdb78f43111250">pcl::ism::ImplicitShapeModelEstimation::TC::max_count_</a></div><div class="ttdeci">int max_count_</div><div class="ttdoc">Defines maximum number of iterations for k-means clustering.</div><div class="ttdef"><b>Definition:</b> implicit_shape_model.h:306</div></div>
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